From Latent Dynamics to Meaningful Representations.

While representation learning has been central to the rise of machine learning and artificial intelligence, a key problem remains in making the learned representations meaningful. For this, the typical approach is to regularize the learned representation through prior probability distributions. However, such priors are usually unavailable or are ad hoc. To deal with this, recent efforts have shifted toward leveraging the insights from physical principles to guide the learning process. In this spirit, we propose a purely dynamics-constrained representation learning framework. Instead of relying on predefined probabilities, we restrict the latent representation to follow overdamped Langevin dynamics with a learnable transition density─a prior driven by statistical mechanics. We show that this is a more natural constraint for representation learning in stochastic dynamical systems, with the crucial ability to uniquely identify the ground truth representation. We validate our framework for different systems including a real-world fluorescent DNA movie data set. We show that our algorithm can uniquely identify orthogonal, isometric, and meaningful latent representations.

Genetic underpinnings of arthropod community distributions in Populus trichocarpa.

Community genetics seeks to understand the mechanisms by which natural genetic variation in heritable host phenotypes can encompass assemblages of organisms such as bacteria, fungi, and many animals including arthropods. Prior studies that focused on plant genotypes have been unable to identify genes controlling community composition, a necessary step to predict ecosystem structure and function as underlying genes shift within plant populations. We surveyed arthropods within an association population of Populus trichocarpa in three common gardens to discover plant genes that contributed to arthropod community composition. We analyzed our surveys with traditional single-trait genome-wide association analysis (GWAS), multitrait GWAS, and functional networks built from a diverse set of plant phenotypes. Plant genotype was influential in structuring arthropod community composition among several garden sites. Candidate genes important for higher level organization of arthropod communities had broadly applicable functions, such as terpenoid biosynthesis and production of dsRNA binding proteins and protein kinases, which may be capable of targeting multiple arthropod species. We have demonstrated the ability to detect, in an uncontrolled environment, individual genes that are associated with the community assemblage of arthropods on a host plant, further enhancing our understanding of genetic mechanisms that impact ecosystem structure.

Loci on chromosome 20 interact with rs16969968 to influence cigarettes per day in European ancestry individuals.

BACKGROUND: The understanding of the molecular genetic contributions to smoking is largely limited to the additive effects of individual single nucleotide polymorphisms (SNPs), but the underlying genetic risk is likely to also include dominance, epistatic, and gene-environment interactions. METHODS: To begin to address this complexity, we attempted to identify genetic interactions between rs16969968, the most replicated SNP associated with smoking quantity, and all SNPs and genes across the genome. RESULTS: Using the UK Biobank European subsample, we found one SNP, rs1892967, and two genes, PCNA and TMEM230, that showed a significant genome-wide interaction with rs16969968 for log10 CPD and raw CPD, respectively, in a sample of 116 442 individuals who self-reported currently or previously smoking. We extended these analyses to individuals of South Asian descent and meta-analyzed the combined sample of 117 212 individuals of European and South Asian ancestry. We replicated the gene findings in a meta-analysis of five Finnish samples (N=40 140): FinHealth, FINRISK, Finnish Twin Cohort, GeneRISK, and Health-2000-2011. CONCLUSIONS: To our knowledge, this represents the first reliable epistatic association between single nucleotide polymorphisms for smoking behaviors and provides a novel direction for possible future functional studies related to this interaction. Furthermore, this work demonstrates the feasibility of these analyses by pooling multiple datasets across various ancestries, which may be applied to other top SNPs for smoking and/or other phenotypes.

Disentangling differing relationships between internalizing disorders and alcohol use.

Both internalizing disorders and alcohol use have dramatic, wide-spread implications for global health. Previous work has established common phenotypic comorbidity among these disorders, as well as shared genetic variation underlying them both. We used genomic structural equation modeling to investigate the shared genetics of internalizing, externalizing, and alcohol use traits, as well as to explore whether specific domains of internalizing symptoms mediate the contrasting relationships with problematic alcohol use compared to alcohol consumption. We also examined patterns of genetic correlations between similar traits within additional Finnish and East Asian ancestry groups. When the shared genetic influence of externalizing psychopathology was accounted for, the genetic effect of internalizing traits on alcohol use was reduced, suggesting the important role of common genetic factors underlying multiple psychiatric disorders and their genetic influences on comorbidity of internalizing and alcohol use traits. Individual internalizing domains had contrasting effects on frequency of alcohol consumption, which demonstrate the complex system of pleiotropy that exists, even within similar disorders, and can be missed when evaluating only relationships among formal diagnoses. Future work must consider the broad effects of shared psychopathology along with the fine-scale effects of heterogeneity within disorders to more fully understand the biology underlying complex traits.

Population links between an insectivorous bird and moths disentangled through national-scale monitoring data.

Insects are key components of food chains, and monitoring data provides new opportunities to identify trophic relationships at broad spatial and temporal scales. Here, combining two monitoring datasets from Great Britain, we reveal how the population dynamics of the blue tit Cyanistes caeruleus are influenced by the abundance of moths - a core component of their breeding diet. We find that years with increased population growth for blue tits correlate strongly with high moth abundance, but population growth in moths and birds is less well correlated; suggesting moth abundance directly affects bird population change. Next, we identify moths that are important components of blue tit diet, recovering associations to species previously identified as key food sources such as the winter moth Operoptera brumata. Our work provides new evidence that insect abundance impacts bird population dynamics in natural communities and provides insight into spatial diet turnover at a national-scale.

Poly-omic risk scores predict inflammatory bowel disease diagnosis.

Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and selection operator regression to select features for each omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between data sets. Individually, metabolomic and viromic scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke's R2 of 0.46 and an area under the curve of 0.80 (95% confidence interval: 0.63, 0.98). Our work supports that some single-omic models for complex traits are more predictive than others, that incorporating multiple omic data sets may improve prediction, and that each omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic data sets.IMPORTANCEComplex traits are characterized by many biological and environmental factors, such that multi-omic data sets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each omic data set in single-omic versus multi-omic models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple omic data types to disentangle complex disease etiologies and biological signatures.

Testing associations between human anxiety and genes previously implicated by mouse anxiety models.

Anxiety disorders are common and can be debilitating, with effective treatments remaining hampered by an incomplete understanding of the underlying genetic etiology. Improvements have been made in understanding the genetic influences on mouse behavioral models of anxiety, yet it is unclear the extent to which genes identified in these experimental systems contribute to genetic variation in human anxiety phenotypes. Leveraging new and existing large-scale human genome-wide association studies, we tested whether sets of genes previously identified in mouse anxiety-like behavior studies contribute to a range of human anxiety disorders. When tested as individual genes, 13 mouse-identified genes were associated with human anxiety phenotypes, suggesting an overlap of individual genes contributing to both mouse models of anxiety-like behaviors and human anxiety traits. When genes were tested as sets, we did identify 14 significant associations between mouse gene sets and human anxiety, but the majority of gene sets showed no significant association with human anxiety phenotypes. These few significant associations indicate a need to identify and develop more translatable mouse models by identifying sets of genes that "match" between model systems and specific human phenotypes of interest. We suggest that continuing to develop improved behavioral paradigms and finer-scale experimental data, for instance from individual neuronal subtypes or cell-type-specific expression data, is likely to improve our understanding of the genetic etiology and underlying functional changes in anxiety disorders.

The relationships between perceived mental illness prevalence, mental illness stigma, and attitudes toward help-seeking.

Previous research suggests that, despite the commonality of mental illness in the United States, the majority of U.S. individuals with mental illness do not seek treatment. One important factor that contributes to this lack of treatment utilization is mental illness stigma. Such stigma may result, in part, from many individuals in the U.S. underestimating the prevalence of mental illness. To test whether this is the case, 638 adults from across the U.S. completed measures related to perceived prevalence of mental illness, private stigma, perceived public stigma, and help-seeking. Findings indicated participants significantly underestimated the given-year prevalence rate of mental illness. The perceived given-year prevalence rate was significantly correlated with lower private stigma and more positive attitudes towards help-seeking. Personal stigma significantly predicted attitudes towards help-seeking. Findings also suggested that individuals who have received mental health services have a higher perceived prevalence rate of mental illness, as well as lower levels of personal stigma and more positive attitudes towards help-seeking. These findings support the notion that helping the general public recognize the true prevalence rate of mental illness could reduce personal mental illness stigma and facilitate help-seeking behaviors. However, future experimental studies are needed to test this hypothesis.

Transcriptome-wide gene-gene interaction associations elucidate pathways and functional enrichment of complex traits.

It remains unknown to what extent gene-gene interactions contribute to complex traits. Here, we introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. Using imputed transcriptomes, we simultaneously reduce the computational challenge and improve interpretability and statistical power. We discover (in the UK Biobank) and replicate (in independent cohorts) several interaction associations, and find several hub genes with numerous interactions. We also demonstrate that TWIS can identify novel associated genes because genes with many or strong interactions have smaller single-locus model effect sizes. Finally, we develop a method to test gene set enrichment of TWIS associations (E-TWIS), finding numerous pathways and networks enriched in interaction associations. Epistasis is may be widespread, and our procedure represents a tractable framework for beginning to explore gene interactions and identify novel genomic targets.

Mechanisms underpinning community stability along a latitudinal gradient: Insights from a niche-based approach.

At large scales, the mechanisms underpinning stability in natural communities may vary in importance due to changes in species composition, mean abundance, and species richness. Here we link species characteristics (niche positions) and community characteristics (richness and abundance) to evaluate the importance of stability mechanisms in 156 butterfly communities monitored across three European countries and spanning five bioclimatic regions. We construct niche-based hierarchical structural Bayesian models to explain first differences in abundance, population stability, and species richness between the countries, and then explore how these factors impact community stability both directly and indirectly (via synchrony and population stability). Species richness was partially explained by the position of a site relative to the niches of the species pool, and species near the centre of their niche had higher average population stability. The differences in mean abundance, population stability, and species richness then influenced how much variation in community stability they explained across the countries. We found, using variance partitioning, that community stability in Finnish communities was most influenced by community abundance, whereas this aspect was unimportant in Spain with species synchrony explaining most variation; the UK was somewhat intermediate with both factors explaining variation. Across all countries, the diversity-stability relationship was indirect with species richness reducing synchrony which increased community stability, with no direct effects of species richness. Our results suggest that in natural communities, biogeographical variation observed in key drivers of stability, such as population abundance and species richness, leads to community stability being limited by different factors and that this can partially be explained due to the niche characteristics of the European butterfly assemblage.

From clinical to corporate: opportunities for nurses away from the bedside.

Luke Evans, Education Fellow, UCLPartners, London, meets nurses whose careers have taken them from the NHS to a variety of different roles.

Characterizing the trophy hunting debate on Twitter.

Social media is an arena of debate for contentious political and social topics. One conservation topic debated online is the acceptability of trophy hunting, a debate that has implications for national and international policy. We used a mixed-methods approach (grounded theory and quantitative clustering) to identify themes in the trophy hunting debate on Twitter. We examined commonly co-occurring categories that describe people's stances on trophy hunting. We identified 12 categories and 4 preliminary archetypes opposing trophy hunting-activism, scientific, condemning, and objecting-whose opposition derived from different moral reasoning. Few tweets (22) in our sample of 500 supported trophy hunting, whereas 350 opposed it. The debate was hostile; 7% of tweets in our sample were categorized as abusive. Online debates can be unproductive, and our findings may be important for stakeholders wishing to effectively engage in the trophy hunting debate on Twitter. More generally, we contend that because social media is increasingly influential, it is important to formally contextualize public responses to contentious conservation topics in order to aid communication of conservation evidence and to integrate diverse public perspectives in conservation practice.

Caracterización del debate sobre la cacería de trofeos en Twitter Resumen Las redes sociales son arenas de debate para temas políticos y sociales polémicos. Un tema de conservación que se debate en línea es la aceptación de la cacería de trofeos, cuya discusión tiene implicaciones políticas nacionales e internacionales. Usamos una estrategia de métodos mixtos (teoría fundamentada y datos cuantitativos agrupados) para identificar los temas en el debate sobre la cacería de trofeos en Twitter. Analizamos las categorías concurrentes más comunes que describían la postura de las personas con respecto al tema. Identificamos doce categorías y cuatro arquetipos preliminares en contra de la cacería de trofeos (activista, científico, condenatorio y opositor), cuya oposición derivó de diferentes razonamientos morales. Pocos tuits (22) en nuestra muestra de 500 apoyaban la cacería de trofeos, mientras que 350 se oponían a ella. El debate era hostil, pues 7% de los tuits en nuestra muestra estuvieron categorizados como abusivos. Los debates en línea pueden ser improductivos y nuestros descubrimientos pueden ser importantes para los actores que desean participar de forma efectiva en el debate sobre la cacería de trofeos en Twitter. De manera más generalizada, sostenemos que, debido a la creciente influencia de las redes sociales, es importante que las respuestas públicas a los temas polémicos de conservación estén contextualizadas de manera formal para así auxiliar a la comunicación de la evidencia de conservación y para integrar las diferentes perspectivas públicas en la práctica de la conservación.

An operations research approach to automated patient scheduling for eye care using a multi-criteria decision support tool.

Inefficient management of resources and waiting lists for high-risk ophthalmology patients can contribute to sight loss. The aim was to develop a decision support tool which determines an optimal patient schedule for ophthalmology patients. Our approach considers available booking slots as well as patient-specific factors. Using standard software (Microsoft Excel and OpenSolver), an operations research approach was used to formulate a mathematical model. Given a set of patients and clinic capacities, the model objective was to schedule patients efficiently depending on eyecare measure risk factors, referral-to-treatment times and targets, patient locations and slot availabilities over a pre-defined planning horizon. Our decision support tool can feedback whether or not a patient is scheduled. If a patient is scheduled, the tool determines the optimal date and location to book the patients' appointments, with a score provided to show the associated value of the decisions made. Our dataset from 519 patients showed optimal prioritization based on location, risk of serious vision loss/damage and the referral-to-treatment time. Given the constraints of available slots, managers can input hospital-specific parameters such as demand and capacity into our model. The model can be applied and implemented immediately, without the need for additional software, to generate an optimized patient schedule.

Testing Association of Previously Implicated Gene Sets and Gene-Networks in Nicotine Exposed Mouse Models with Human Smoking Phenotypes.

INTRODUCTION: Smoking behaviors are partly heritable, yet the genetic and environmental mechanisms underlying smoking phenotypes are not fully understood. Developmental nicotine exposure (DNE) is a significant risk factor for smoking and leads to gene expression changes in mouse models; however, it is unknown whether the same genes whose expression is impacted by DNE are also those underlying smoking genetic liability. We examined whether genes whose expression in D1-type striatal medium spiny neurons due to DNE in the mouse are also associated with human smoking behaviors. METHODS: Specifically, we assessed whether human orthologs of mouse-identified genes, either individually or as a set, were genetically associated with five human smoking traits using MAGMA and S-LDSC while implementing a novel expression-based gene-SNP annotation methodology. RESULTS: We found no strong evidence that these genes sets were more strongly associated with smoking behaviors than the rest of the genome, but ten of these individual genes were significantly associated with three of the five human smoking traits examined (p < 2.5e-6). Three of these genes have not been reported previously and were discovered only when implementing the expression-based annotation. CONCLUSIONS: These results suggest the genes whose expression is impacted by DNE in mice are largely distinct from those contributing to smoking genetic liability in humans. However, examining a single mouse neuronal cell type may be too fine a resolution for comparison, suggesting that experimental manipulation of nicotine consumption, reward, or withdrawal in mice may better capture genes related to the complex genetics of human tobacco use. IMPLICATIONS: Genes whose expression is impacted by DNE in mouse D1-type striatal medium spiny neurons were not found to be, as a whole, more strongly associated with human smoking behaviors than the rest of the genome, though ten individual mouse-identified genes were associated with human smoking traits. This suggests little overlap between the genetic mechanisms impacted by DNE and those influencing heritable liability to smoking phenotypes in humans. Further research is warranted to characterize how developmental nicotine exposure paradigms in mice can be translated to understand nicotine use in humans and their heritable effects on smoking.

Hippocampal RNA sequencing in mice selectively bred for high and low activity.

High and Low Activity strains of mice were bidirectionally selected for differences in open-field activity (DeFries et al., 1978, Behavior Genetics, 8: 3-13) and subsequently inbred to use as a genetic model for studying anxiety-like behaviors (Booher et al., 2021, Genes, Brain and Behavior, 20: e12730). Hippocampal RNA-sequencing of the High and Low Activity mice identified 3901 differentially expressed protein-coding genes, with both sex-dependent and sex-independent effects. Functional enrichment analysis (PANTHER) highlighted 15 gene ontology terms, which allowed us to create a narrow list of 264 top candidate genes. Of the top candidate genes, 46 encoded four Complexes (I, II, IV and V) and two electron carriers (cytochrome c and ubiquinone) of the mitochondrial oxidative phosphorylation process. The most striking results were in the female high anxiety, Low Activity mice, where 39/46 genes relating to oxidative phosphorylation were upregulated. In addition, comparison of our top candidate genes with two previously curated High and Low Activity gene lists highlight 24 overlapping genes, where Ndufa13, which encodes the supernumerary subunit A13 of complex I, was the only gene to be included in all three lists. Mitochondrial dysfunction has recently been implicated as both a cause and effect of anxiety-related disorders and thus should be further explored as a possible novel pharmaceutical treatment for anxiety disorders.

Genome-wide Association Study Shows That Executive Functioning Is Influenced by GABAergic Processes and Is a Neurocognitive Genetic Correlate of Psychiatric Disorders.

BACKGROUND: Deficits in executive functions (EFs), cognitive processes that control goal-directed behaviors, are associated with psychopathology and neurologic disorders. Little is known about the molecular bases of individual differences in EFs. Prior candidate gene studies have been underpowered in their search for dopaminergic processes involved in cognitive functioning, and existing genome-wide association studies of EFs used small sample sizes and/or focused on individual tasks that are imprecise measures of EFs. METHODS: We conducted a genome-wide association study of a common EF (cEF) factor score based on multiple tasks in the UK Biobank (n = 427,037 individuals of European descent). RESULTS: We found 129 independent genome-wide significant lead variants in 112 distinct loci. cEF was associated with fast synaptic transmission processes (synaptic, potassium channel, and GABA [gamma-aminobutyric acid] pathways) in gene-based analyses. cEF was genetically correlated with measures of intelligence (IQ) and cognitive processing speed, but cEF and IQ showed differential genetic associations with psychiatric disorders and educational attainment. CONCLUSIONS: Results suggest that cEF is a genetically distinct cognitive construct that is particularly relevant to understanding the genetic variance in psychiatric disorders.

Relationship of Conflict, Conflict Avoidance, and Conflict Resolution to Psychological Adjustment.

Background: Conflict has deleterious effects on the adjustment of children, adolescence, and emerging adults. The literature is less robust on the adverse effects of conflict avoidance on adjustment as well as the beneficial effect of resolution in these age groups. The literature is markedly sparser on these relationships in adults. Method: We recruited N = 1471 US adults between 18 and 86 years old (M = 33.94, SD = 11.67). They primarily identified as White with 51% holding at least a bachelor's degree. Participants responded to the Perceived Family Conflict Subscale, Avoidant Conflict Scale, the Family Conflict Resolution Scale, and the Langner Symptom Survey. Results: A MANOVA modeling the four variables demonstrated a significant difference based on sex for the measure of distress and need for treatment (Mwomen = 5.31, Mmen = 3.93, p < .001). Separate analyses for men and women yielded the same pattern for each sex. Specifically, low conflict groups, as well as low conflict avoidance groups, scored significantly lower on a measure of distress than the high conflict and conflict avoidance group. For conflict resolution, the high groups scored significantly lower on distress than did the low resolution group. Discussion: The deleterious effect of conflict and conflict avoidance were found in both adult men and women, thereby extending results found in children, teens, and young adults. Similarly, the beneficial effect of conflict resolution manifest in adult men and women, which had been found in young adults. Interventions aimed at reducing conflict avoidance and increasing conflict resolution skills should theoretically reduce stress.

Examination of a novel expression-based gene-SNP annotation strategy to identify tissue-specific contributions to heritability in multiple traits.

Complex traits show clear patterns of tissue-specific expression influenced by single nucleotide polymorphisms (SNPs), yet current strategies aggregate SNP effects to genes by employing simple physical proximity-based windows. Here, we examined whether incorporating SNPs with effects on tissue-specific cis-expression would improve our ability to detect trait-relevant tissues across 31 complex traits using stratified linkage disequilibrium score regression (S-LDSC). We found that a physical proximity annotation produced more significant tissue enrichments and larger S-LDSC regression coefficients, as compared to an expression-based annotation. Furthermore, we showed that our expression-based annotation did not outperform an annotation strategy in which an equal number of randomly chosen SNPs were annotated to genes within the same genomic window, suggesting extensive redundancy among SNP effect estimates due to linkage disequilibrium. That said, current sample sizes limit estimation of cis-genetic SNP effects; therefore, we recommend reexamination of the expression-based annotation when larger tissue-specific expression datasets become available. To examine the influence of sample size, we used a large whole blood eQTL reference panel (N = 31,684) applying a similar expression-based annotation strategy. We found that significant cis-expression QTLs in whole blood did not outperform the physical proximity annotation when estimating tissue-specific SNP heritability enrichment for either high- or low-density lipoprotein phenotypes but performed similarly for inflammatory bowel disease. Finally, we report new and updated tissue enrichment estimates across 31 complex traits, such as significant heritability enrichment of the frontal cortex for cognitive performance, educational attainment, and intelligence, providing further evidence of this structure's importance in higher cognitive function.